Method of visualizing a bridge therapy process

Abstract

The present invention provides for a simultaneous graphical representation, a risk of bleeding and a risk of thrombosis providing a visualized bridge therapy process. Furthermore, the present invention provides for a computer-based prediction of the haemostatic situation of the examined blood circulation by using a combination of a biochemical model and a pharmacokinetic model for calculation or another mathematical representation of the blood circulation.

Claims

1. A method of visualizing a bridge therapy process, the method comprising the steps of: receiving coagulation data describing a haemostatic situation of a blood circulation of a patient at a computer having a processor and a display, and displaying, on the display of the computer, the coagulation data to a user by means of a graphical representation, the displaying including: displaying a first value measured by a first type of blood test, wherein the first value indicates a first effect of a heparin like drug on the haemostatic situation, and simultaneously to the displaying of the first value displaying a second value measured by a second type of blood test, wherein the second value indicates a second effect of a vitamin K antagonist type anticoagulant on the haemostatic situation, and wherein the first and second values describe the haemostatic situation of the blood circulation at a first point in time and are simultaneously displayed as a first point within an X and Y coordinate system having the first value for the X axis and the second value for the Y axis.

2. The method according to claim 1, the displaying of the coagulation data further comprising the steps of: displaying, on the display of the computer, a third value measured by the first type of blood test, wherein the third value indicates a third effect of a heparin like drug on the haemostatic situation, simultaneously to the displaying of the third value displaying, on the display of the computer, a fourth value measured by the second type of blood test, wherein the fourth value indicates a fourth effect of a vitamin K antagonist type anticoagulant on the haemostatic situation, wherein the third and fourth values describe the haemostatic situation of the blood circulation at a second point in time and are simultaneously displayed as a second point within the X and Y coordinate system having the third value for the X axis and the fourth value for the Y axis.

3. The method according to claim 1, wherein the first and second type of blood test are respectively and independently chosen from the group consisting of activated partial thrombopiastine time (aPTT) test, anti-Factor 10a test, prothrombin time (PT) test, international normalized ratio (INR) test, a test indicating thrombosis or coagulation, a test indicating bleeding or anti-coagulation, a test indicating the haemostatic function.

4. The method according to claim 1, further comprising the step of: calculating, using the processor of the computer, a prediction of the haemostatic situation of the blood circulation by a mathematical representation of the blood circulation.

5. The method according to claim 1, further comprising the step of: calculating, using the processor of the computer, a prediction of the haemostatic situation of the blood circulation by combining a biochemical model of the blood circulation and a pharmacodynaniical model of the blood circulation.

6. The method according to claim 5, further comprising the steps of: calculating, using the processor of the computer, an effect of an anticoagulant drug during a predetermined time by using the pharmacodynamical model, to generate a calculated pharmacodynamical effect.

7. The method according to claim 6, the method further comprising the steps of: calculating, using the processor of the computer, a coagulation effect and/or a fibrin polymerization effect with the biochemical model based on the calculated pharmacodynamical effect, and displaying, on the display of the computer, a prediction value of the haemostatic situation, wherein the prediction value is based on at least one of the calculated pharmacodynamical effect, the calculated coagulation effect, and the calculated fibrin polymerization effect.

8. The method according to claim 1, the method further comprising the step of: automatically suggesting an application of a coagulant and/or an anti-coagulant.

9. The method according to claim 8, wherein the step of automatically suggesting an application of a coagulant and/or an anti-coagulant is based on a calculated progression of the haemostatic situation.

10. The method of claim 1 further comprising the step of: displaying a rectangle in the X and Y coordinate system depicting a region in which a safe haemostatic situation is indicated.

11. A user interface device for visualizing a bridge therapy process, the user interface comprising: a user interface configured to receive coagulation data describing a haemostatic situation of a blood circulation of a patient, a display configured to display the haemostatic situation of said blood circulation including: displaying a first value measured by a first type of blood test, wherein the first value indicates a first effect of a heparin like drug on the haemostatic situation, and simultaneously displaying a second value measured by a second type of blood test, wherein the second value indicates a second effect of a vitamin K antagonist type anticoagulant on the haemostatic situation; wherein the first and second values describe the haemostatic situation of the blood circulation at a first point in time; and wherein the first and second values are simultaneously displayed as the first point within a two-dimensional coordinate system having an axis for the first type of blood test and a different axis for the second type of blood test.

12. The user interface device according to claim 11 wherein the user interface is configured to display a time progression of the haemostatic situation of said blood circulation by further: displaying a third value measured by the first type of blood test, wherein the third value indicates a third effect of a heparin like drug on the haemostatic situation at a second point in time, and displaying a fourth value measured by the second type of blood test simultaneously to the first, second and third value, wherein the fourth value indicates a fourth effect of a vitamin K antagonist type anticoagulant on the haemostatic situation at the second point in time, and wherein the third and fourth values describe the haemostatic situation of the blood circulation at the second point in time.

13. The user interface device according to claim 11, further comprising: a computer processor configured to calculate a prediction of the haemostatic situation of the blood circulation by using mathematical representation of the blood circulation or by a combining a biochemical model of the blood, circulation and a pharmacodynamical model of the blood circulation.

14. The user interface device according to claim 11, wherein the computer processor is configured to: calculate an effect of an anticoagulant drug during a predetermined time by using the pharmacodynamical model, generate a calculated pharmacodynamical effect, and use the calculated pharmacodynamical effect as an input for the biochemical model.

15. The user interface device of claim 11 wherein the display is configured to further display a rectangle in the two-dimensional coordinate system depicting a region in which a safe haemostatic situation is indicated.

16. A non-transitory computer readable medium in which a program element for visualizing a bridge therapy process is stored, which, when being executed by a computer including a processor and a display performs a method including: receiving coagulation data describing a haemostatic situation of a blood circulation including at least: a first value measured at a first point in time by a first type of blood test that indicates an effect of a heparin like drug on the haemostatic situation, and a second value measured at the first point in time by a second type of blood test that indicates an effect of a vitamin K antagonist type anticoagulant on the haemostatic situation, displaying, on the display, a two-dimensional coordinate system having a first axis representing the first type of blood test and a second axis representing the second type of blood test, and plotting a point in the two-dimensional coordinate system representing the effect at the first point in time of both the heparin like drug and the vitamin K antagonist type anticoagulant, the plotted point having the first value on the first axis and the second value on the second axis.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) Exemplary embodiments of the description will be described in the following drawings:

(2) FIGS. 1 to 3c schematically show a respective flow diagram of a method according to exemplary embodiments of the invention.

(3) FIG. 4 schematically shows a user interface according to an exemplary embodiment of the invention.

(4) FIG. 5 schematically shows a user interface according to an exemplary embodiment of the invention.

(5) FIG. 6 schematically shows an graphical representation as it can be used during an exemplary embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

(6) FIG. 1 shows a flow diagram of a method of visualizing a bridge therapy process wherein the presented method comprises steps (S1) to (S4). In step (S1) coagulation data describing haemostatic situation of a blood circulation of a patient is received. Such data may for example be received by a user interface or by a calculation arrangement or a computer system. Furthermore, the coagulation data comprise measured or simulated values of tests that indicate the haemostatic situation of a blood circulation. Therein values of an aPTT test, a PT test, an INR test, a test indicating thrombosis or coagulation, a test indicating bleeding or anticoagulation, a test indicating the haemostatic function and any test combination thereof, may be comprised by said term “coagulation data”.

(7) Furthermore, the method depicted in FIG. 1 comprises the step (S2) which represents a displaying of the coagulation data to a user by means of a graphical representation. Furthermore, the step of displaying a first measured or simulated value of a first type of blood test which first measured or simulated values indicates a first effect of a heparin-like drug on the haemostatic situation is comprised as step (S3) by the method of FIG. 1. Furthermore, step (S4) is comprised which is the step of simultaneously displaying a second measured or simulated value of a second type of blood test to the displaying of the first measured value. The second measured or simulated value indicates a second effect of a vitamin K antagonist type anticoagulant on the haemostatic situation. Furthermore, the first and second measured or simulated values describe a haemostatic situation of the blood circulation at one specific point in time.

(8) Due to the simultaneous display of first and second values which originate from blood tests both effects caused by heparin-like drugs and effects caused by vitamin K antagonist type anticoagulant drugs can be monitored and are displayed to a user. Therefore, it is ensured that the balance between thrombosis and bleeding risk is detected by the clinician supported by this method of visualizing a bridging therapy process.

(9) The first type of blood test might be a PT/INR test which might be relatively insensitive to the effects of the heparin-like drugs but might be sensitive to monitor the effect of VKAs. As with the present invention, not only a constant value for this test is targeted during bridging, an overcompensation of the coagulation and an unacceptably high risk of bleeding is successfully avoided by the present invention. The second type of blood test might for example be embodied by an aPTT blood test, which might be somewhat sensitive to the anticoagulant effect of the VKAs but may certainly not be optimized to monitor VKA therapy but might be sensitive to monitor the effect of heparin like drugs. As the present invention is not targeting a constant value of this test during bridging, it is avoided that an over anticoagulation and a high unacceptable risk of bleeding establishes. The present invention may simultaneously target measurements of the first blood test which are related to vitamin K antagonists type anticoagulant drugs and simultaneously targets measurements of heparin like drugs by the second type of blood test, which increases the reliability and precision of the bridge therapy support provided by the present invention.

(10) If desired, the first measured value may be depicted as a value of an X-axis of an X and Y coordinate system. The second measured value may be depicted as a value of a Y-axis of said X and Y coordinate system. This may be gathered from the following FIG. 6. The graphical representation used during said method may comprise a functionality in which the user is alerted if the analyzed and observed haemostatic situation is evolving into a risk of thrombosis or a risk of bleeding. The user might be alerted by means of an optical, an acoustical or an electrical signal.

(11) Furthermore, the graphical representation used in the presented method may be adapted in such a way that a risk of coagulation and a risk of bleeding of said analyzed blood circulation is simultaneously depicted. In other words, the displaying steps of the method of FIG. 1, steps (S2), (S3) and (S4) may be understood to show a coagulation risk based on at least one measured parameter chosen from the group consisting of aPTT, anti-Factor 10a, PT, INR), a parameter indicating thrombosis or coagulation, a parameter indicating bleeding or anticoagulation, a parameter indicating haemostatic function and any combination thereof. Furthermore, the displaying steps may be seen as to display a risk of bleeding to a user based on at least one measured parameter chosen from the group consisting of activated partial thromboplastine time (aPTT) test, anti-Factor 10a test, prothrombin time (PT) test, international normalized ratio (INR) test, a test indicating thrombosis or coagulation, a test indicating bleeding or anti-coagulation, a test indicating haemostatic function and any test combination thereof.

(12) As both types of blood tests are performed at essentially the same point in time, reliable information about the haemostatic situation of the patient, at said specific point in time is possible.

(13) If desired, the presented method of FIG. 1 can be extended to again perform the steps in a second round at a second point in time. Afterwards, a time-resolved progression of the risk of coagulation of said blood circulation and the risk of bleeding of said blood circulation may be displayed in the graphical representation.

(14) If desired, both described embodiments regarding FIG. 1 may also be extended by a step of entering a test value into a user interface by the user. This may allow a user to calculate on the basis of a used mathematical model which may comprise a biochemical model and a pharmacokinetic model, how, for example the bleeding risk or the thrombosis risk has evolved, or will evolve if the present bridging treatment is continued without change, since the last retrieval of first and second measured values.

(15) Furthermore, the user may enter into a user interface a changed administration of, for example, a heparin-like drug a vitamin K antagonist-type anticoagulant in order to obtain a predicted calculated progression of the analyzed blood circulation of a patient with regard to the bleeding and thrombosis risk. Calculation of such a prediction might be based on a mathematical model comprising a biochemical model and a pharmacodynamical model will be described hereinafter.

(16) If desired, the results of the first and second type of blood tests of FIG. 1 may also be computer generated. The herein described mathematical model might be used in the context of the present invention which model takes into account biochemical aspects and pharmacodynamical aspects of characteristics of the examined or monitored blood circulation. In other words the method of visualizing a bridge therapy process may also be completely based on simulated values of a first and second type of blood tests. However, if desired, a combination of measured values taken from a blood sample and simulated, calculated or modeled values is comprised in the present invention. Also only measured values may be used to monitor the time progression of the values of the first and second blood tests.

(17) FIG. 2 schematically shows another method of visualizing a bridging therapy process according to another exemplary embodiment of the invention. Regarding the shown steps (S1) to (S4) it is referred to the complete description regarding FIG. 1 which was previously presented. In addition to the steps (S1) through (S4), FIG. 2 shows steps (S5) and (S6). The method of FIG. 2 comprises a displaying of a third measured or simulated value of the first type of blood test, which third measured or simulated value indicates a third effect of a heparin-like drug on the haemostatic situation. This third effect might be similar to the first effect, however it takes place or has taken place at another point in time as the third measured or simulated value is measured or simulated in or for a different point in time compared to the first measured value. Furthermore, the presented method comprises the step of simultaneously displaying a fourth measured or simulated value of a second type of blood test simultaneously to the displaying of the third measured value. The fourth measured value indicates a fourth effect of vitamin K antagonist type anticoagulant on the haemostatic situation and thereby displays a time progression of the haemostatic situation.

(18) In other words, the visualizing method of FIG. 2 displays to the user, two distinct points in an X and Y coordinate system, wherein the X axis may relate to aPTT values and the Y axis to INR values. However, the person skilled in the art directly and unambiguously derives from this exemplary embodiment that also other tests may be used in such an exemplary X and Y coordinate system representation to display the risk of thrombosis and the risk of bleeding in a proper and efficient way to the user. So, time progression of the risk of bleeding and risk of thrombosis is displayable to the user. This might be extended by a computer model allowing for predictions based on for example entering assumptions like changing INR values or a desired change in the dosage regime for used drugs.

(19) In other words, FIG. 2 is an abstract representation of what can be seen in FIG. 6, right hand side, in the aPTT and INR coordinate system. Each depicted point in said coordinate system can be interpreted as a first measured value and a second measured value with regard to the first type of blood test being aPTT and with regard to a second type of blood test being INR.

(20) FIGS. 3a to 3c respectively describe an exemplary method as an embodiment of the present invention. FIG. 3a shows a method of visualizing a bridge therapy process in which (S1) to (S4) are the steps that have been described with regard to FIG. 1. After step (S4) has been performed by the method of FIG. 3a, the method becomes a computer support decision method as step (S9) is performed. Step (S9) describes calculating a prediction of the haemostatic situation of the blood circulation by combining a biochemical model of the blood circulation and a pharmacodynamical model of the blood circulation.

(21) In the exemplary embodiment of FIG. 3b an effect of an anticoagulant drug during a predetermined time is calculated by using the above described pharmacodynamical model in step (S10). The calculation results in a calculated pharmacodynamical effect. If desired, such an effect may be used later in other exemplary embodiments of the invention. In other words, the exemplary embodiment of FIG. 3b comprises a mathematical model which only takes into account the pharmacodynamical model in order to assist the bridging process. In other words the hemostatic risk is based directly on the modeled drug concentration in the blood or the resulting changes in active coagulation proteins.

(22) If desired, also only the biochemical model might be used in a calculation step for predicting the haemostatic situation. However, such an embodiment in which only the biochemical model is used is not shown.

(23) The exemplary embodiment of FIG. 3c shows a use of a mathematical model which takes into account a pharmacodynamical model in step (S10), in order to base the prediction on absorption, distribution, metabolism, and excretion properties of the patient's response to the drugs. The examined blood circulation system may comprise several vessels. Furthermore, the distribution dynamics and/or distribution kinetics of a drug in said vessel system is considered as the pharmacodynamical model might have been generated based on these aspects.

(24) The pharmacodynamical model may be based not only on distribution in the vessels but in the whole body. So it starts with absorbing the drug, than distribution of the drug occurs throughout the body also leading to a certain amount of drug in the blood circulation, and so on. The amount of drug in the blood may be a parameter important to the presented model.

(25) Furthermore, a biochemical model is used for calculating the prediction of the haemostatic situation in step (S11). The biochemical model calculates a coagulation effect and/or a fibrin polymerization effect based on the calculated pharmacodynamical effect in step (S11). If desired a prediction value of the haemostatic situation may be displayed to a user in step (S12). Therein, the prediction value is based on at least one of the calculated pharmacodynamical effects, the calculated coagulation effect and the calculated fibrin polymerization effect. In a complete version all three effects are used as basis for the prediction. As can bee seen from the comprised Tables 1 to 5, the coagulation cascade is considered (Table 1), fibrin polymerization is considered (Table 2), effects of unfractionated heparin (UFH) and low-molecular weight heparin (LMWH) or other heparin-like drugs (Table 3) are considered by means of the mathematical model. Furthermore, in Tables 4 and 5 ordinary differential equations that might be incorporated partially or also completely in the mathematical model regarding pharmacodynamical effects of unfractionated heparin (UFH) and LMWH. Furthermore, Table 5 shows ordinary differential equations which might be incorporated in the mathematical model if desired. The equations regard pharmacokinetic and pharmacodynamical effects of Warfarin, which is a vitamin K antagonist (VKA).

(26) In other words the mathematical model can, if desired, comprise partially or completely the reaction mechanisms that are disclosed in Tables 1 to 3. Also the ordinary differential equations disclosed in Tables 4 and 5 may be integrated into the mathematical model according to the user's desire. In other words, also a combination between different reaction mechanisms out of Tables 1 to 3 with ordinary differential equations out of Tables 4 and 5 are possible. In other words the person skilled in the art may take from the presented tables 1 to 5 the features he is interested in regarding his special medical case. Thus, it is made clear that the model presented herein is just a version of the model, and that this model can be adapted, extended, reduced or even completely replaced by another mathematical model which takes into account biochemical and pharmacodynamical aspects.

(27) The gist of this mathematical model can be seen in the combination of a biochemical model calculating coagulation cascade and fibrin polymerization. Thus, “enzymatic conversion” in combination with “complex assembly” can be taken into account during the prediction.

(28) The mathematical model used in the context of the present invention will be explained in more detail hereinafter. This model may be implemented in every herein described embodiment of the invention. It is of utmost importance that the below presented model is just one exemplary embodiment of the mathematical model according to the present invention.

(29) Mathematical Definition of the Model

(30) The mathematical model can be considered to consist of three separate modules: coagulation cascade, fibrin polymerization and pharmacokinetics and pharmacodynamics (PK/PD) of anticoagulant drugs. Whereas the first two modules are based on the underlying (protein) interactions of the coagulation response and are used to simulate in vitro tests like the thrombin generation assay, prothrombin time (PT) and activated partial thromboplastin time (aPTT), the latter is based on compartment modeling which is used to simulate the (long-term) kinetics and effect of the anticoagulants such as unfractionated heparin (UFH), low-molecular weight heparin (LMWH) and warfarin in the human body. The way this is coupled is as follows: the effect of the anticoagulants is simulated for the whole bridging period, at predefined time points a ‘snapshot’ is taken of the disturbed situation in the blood (symbolizing a blood sample taken from the patient) and used as input to the biochemical model of the coagulation cascade and fibrin polymerization.

(31) Biochemical Model

(32) The physiological system of biochemical reactions may be represented as a closed volume element, representing a certain volume of blood plasma in in vitro tests. Hence, there is no transport in or out of this volume and clearance of proteins is assumed to be not significant on this time-scale (minutes). This means that there is conservation of mass in the volume element. Besides that it is assumed that diffusion in the mixture does not significantly influence the reaction velocities.

(33) The mathematical model of the coagulation cascade and fibrin polymerization consists of 216 state variables (concentrations of proteins and protein complexes) and 100+ reaction rate constants that are used to parameterize 91 reactions. An overview of the reactions is given in Table 1, Table 2 and Table 3. All states, initial concentrations and kinetic parameters were defined as non-negative real numbers, IR+0. The initial concentrations of the proteins are inferred from values reported in literature or set to the actual measured concentrations. The model's kinetic parameters were estimated from in-house generated experimental data by means of solving the inverse problem. Nevertheless, the kinetic parameters (but also the initial concentrations) are subjected to a continuous update process to improve the accuracy of the found values by means of additional experiments and analyses. The functional description of the state equations can be represented as follows (in state space formulation).

(34) $\begin{array}{cc}\frac{dx}{dt}=f\left(x\left(t\right),u\left(t\right),\theta \right),\mathrm{with}{x}_0=x\left(t_0\right)& \left(1\right)\end{array}$

(35) Where x is the state vector, u the input vector of the test conditions (e.g. certain tissue factor concentration to simulate the PT), x0 is the vector of initial concentrations and f is a vector field with non-linear functions parameterized with θ. The output, y, of the state model can be characterized by:
y(t,θ)=Cx(t,θ)  (2)

(36) Where matrix C selects a number of ‘interesting’ states of the model output. The 91 reaction mechanisms derived from literature were classified as either one of two types of elementary reaction mechanisms. These reaction mechanisms were complex assembly and enzymatic conversion.

(37) Complex assembly is the process where substrate A and B react to form complex A-B. It features in the formation of coagulation complexes (e.g. FXa-FVa, FIXa-FVIIIa) and inhibition of activated proteins by stochiometric inhibitors (e.g. FIIa-AT-III, TF-FVIIa-FXa-TFPI). The related reaction equation reads:

(38) $\begin{array}{cc}A+B\begin{array}{c}\stackrel{k_1}{->}\\ \underset{k_{-1}}{\leftarrow }\end{array}A-B& \left(3\right)\end{array}$

(39) The association rate constant of complex formation, k1, is a second order rate constant and the dissociation rate constant of A and B from A-B, k−1, is a first-order rate constant. In some cases the association reaction is irreversible, which means the complex is stable and will not dissociate, e.g. inhibition of FIIa by AT-III. Reaction scheme (3) was converted to the following set of ordinary differential equations (ODEs) describing the change in concentration, represented by [ . . . ], in time:

(40) $\begin{array}{cc}\frac{\partial \left[A\right]}{\partial t}=\frac{\partial \left[B\right]}{\partial t}=-\frac{\partial \left[A-B\right]}{\partial t}=-k_1\left[A\right]\left[B\right]+k_{-1}\left[A-B\right]& \left(4\right)\end{array}$

(41) The enzymatic conversion of proteins by enzymes was the second type of reaction mechanism exploited in the coagulation model. All activation processes in the hemostasis model correspond to this type of reaction. The reaction scheme of enzymatic conversion can be represented schematically as:

(42) $\begin{array}{cc}E+S\begin{array}{c}\stackrel{k_1}{->}\\ \underset{k_{-1}}{\leftarrow }\end{array}E-S\stackrel{k_2}{->}E+P& \left(5\right)\end{array}$

(43) Where E is the enzyme and S the substrate concentration that is converted into product P by E. Enzymatic conversion of proteins was implemented in the mathematical model as follows:

(44) $\begin{array}{cc}\frac{\partial \left[E\right]}{\partial t}=-k_1\left[E\right]\left[S\right]+k_{-1}\left[E-S\right]+k_2\left[E-S\right]& \left(6\right)\\ \frac{\partial \left[S\right]}{\partial t}=-k_1\left[E\right]\left[S\right]+k_{-1}\left[E-S\right]& \left(7\right)\\ \frac{\partial \left[E-S\right]}{\partial t}=k_1\left[E\right]\left[S\right]-k_{-1}\left[E-S\right]-k_2\left[E-S\right]& \left(8\right)\\ \frac{\partial \left[P\right]}{\partial t}=k_2\left[E-S\right]& \left(9\right)\end{array}$

(45) Most of the proteins or protein-complexes participate in multiple reactions in the biochemical model, hence all reactions that the protein or protein-complex is participating in have to be accounted for in the ODE of that specific protein's or protein-complex' concentration. This results in one ODE per protein or protein-complex, which consists of a summation of ODE contributions from all reactions that the protein is participating in. This is represented mathematically as follows (an alternative representation of equation (1)).

(46) $\begin{array}{cc}\frac{\partial x}{\partial t}=\underset{\_}{S}\underset{\_}{R}\left(x\right)=\underset{i=1}{\overset{m}{.Math.}}{S}^i{R}_i\left(x\right)& \left(10\right)\end{array}$

(47) Where x is the vector of concentrations of the different substrates, S is the matrix with reaction rate constants and R is the reaction matrix. Each column of the stochiometric matrix Si corresponds to a particular reaction.

(48) PK/PD Model

(49) The mathematical model that simulates the long-term kinetics and effects of the anticoagulants is based on a combination of compartment models that are generally used in PK/PD modeling. Since the PK/PD equations are not as standardized as the biochemical equations (only complex assembly and enzymatic catalysis), the complete ODEs of each state are shown in Table 4 and Table 5. The ODEs belonging to the pharmacokinetic properties of unfractionated heparin and low-molecular weight heparin are shown in Table 4. As a result of these ODEs the blood kinetics of both types of heparin can be calculated. The effects of both heparins are on the activity of AT-III, and this is represented by equations v78 v91 in the biochemical model, which uses the blood concentrations of UFH and LMWH at the moment of blood withdrawal as input. The ODEs corresponding to the blood kinetics of warfarin and its effect on the production of several coagulation proteins by inhibiting the vitamin K cycle are given in Table 5. The blood concentrations of the coagulation proteins at the moment of blood withdrawal are used as input for the biochemical model.

(50) The Tables 1 to 5 are shown in the following. The mathematical model described herein may thus take into account several or all reaction mechanisms v1 to v91. The person skilled in the art will combine them as needed or desired. Additionally the ordinary differential equations described under PKPD1 to PKPD17 may partially or completely be implemented in the mathematical model.

(51) The used model may also be described as follows: The computer model may be seen as a representation of the coagulation cascade and fibrin polymerization as a set of reaction mechanisms. The time dynamics of each reaction mechanism may be described as an ordinary differential equation or ODE that involves the concentration(s) of the protein(s) and/or chemical molecule(s) that are involved in the reaction and the reaction rate parameter(s). By summation of all reaction mechanisms in which a particular protein or other kind of chemical molecular is involved (a protein or molecule can participate in more than one reaction), the time dynamics of the concentration of that particular protein or other kind of chemical entity may be calculated. Doing this for all proteins or molecules, the whole system can calculate and keep track of the evolution of all proteins and molecules over time, however for this one may require, beside the reaction topology, also the numerical values of the model parameters. These model parameters include the initial conditions of the system, i.e. the concentration of all proteins and molecules at t=0 (e.g. before onset of bridging therapy), and the reaction rate parameters of the reaction mechanisms. Part of the initial concentrations that are the most important to the outcome of the system are measured from the patient (in the laboratory or clinic), whereas others, less determining proteins, are taken from literature (average patient values, possibly corrected for gender and age, etc.). The reaction rate parameters may be derived via solving an inverse problem, i.e. model fitting to experimental data. The system of coupled ODEs may be solved numerically, using the numerical values of the model parameters, by employing standard ODE integration algorithms.

(52) The expected future evolution of the aPTT and INR, as predicted by the computer model, are shown on the user interface together with the evolution of the measured aPTT and INR during the bridging period, which are/were entered in the user interface by the user. These aPTT- and INR-predictions are calculated by the computer model based on the initial concentrations that are determined in the tests, prederived reaction rate parameters and population average values for the unknown concentrations. Previously measured aPTT and INR values in combination with other (clinical) measurements, such as the activity of the vitamin K-dependent proteins, liver function, are used to optimize the predictions to be more patient specific, i.e. personalized therapy planning.

(53) The progression of the measured and predicted aPTT and INR during the bridging period is referred to as bridging path (see representation in the upper right corner of the user interface example of FIG. 6). The user can use the combined measured and predicted bridging path to assess the patient's risk and make appropriate adaptations to the therapy plan. The different therapy options can be simulated by the computer model and the predicted effects thereof are visualized on the user interface.

(54) An improvement of the said model would be to link the application directly to the hospital IT system. In this way the laboratory measurements, patient records and decided therapy plan can be directly communicated to the appropriate other applications of the hospital IT system, or vice versa. Another embodiment could be to replace the aPTT and/or INR tests by other tests that are better estimates of the effect of heparin and VKA treatment. For example, the anti-Factor 10a assay is generally used to monitor the effects of LMWH. In case the patient is treated with LMWH this would suggest to use the output of the anti-factor 10a assay on the x-axis instead of the aPTT to monitor the effect of LMWH. In addition, in the future new and better tests reflecting the hemostatic balance might be invented. Such test may also be used in combination with the present invention.

(55) TABLE-US-00001 TABLE 1 All reaction mechanisms incorporated in the computer model of the coagulation cascade. It should be noted that in this table the official gene symbole are used instead of the popular scientific names Reac- Cofactors/ tion Name Type Substrates Products Catalyst Reaction site v1 F3-F7a complex assembly Complex F3, F7a F3-F7a Endothelial assembly membrane v2 F3-F7 complex assembly Complex F3, F7 F3-F7 Endothelial assembly membrane v3 F7 activation (1) Catalysis F7 F7a F3-F7a Endothelial membrane v4 F7 activation (2) Catalysis F7 F7a F10a Endothelial membrane v5 F7 activation (3) Catalysis F7 F7a F9a Endothelial membrane v6 F7 activation (4) Catalysis F7 F7a F2a v7 F9 activation (1) Catalysis F9 F9a F11a, negative phospholipids v8 F9 activation (2) Catalysis F9 F9a F3-F7a Endothelial membrane v9 F9a degradation Degradation F9a Blood plasma v10 F8 activation (1) Catalysis F8 F8a F2a Blood plasma?? v11 F8 degradation Degradation F8a PROCa-PROS1- Platelet F5ac membrane v12 F9a-F8a complex assembly Complex F9a, F8a F9a-F8a Ca2+, neg Platelet assembly phospholipid membrane v13 F2 activation (1) Catalysis F2 F2a F10a Blood plasma v14 F2 activation (2) Catalysis F2 F2a F10a-F5a Platelet membrane v15 F2a degradation Degradation F2a Blood plasma v16 F5 activation Catalysis F5 F5a F2a Blood plasma v17 F5 anticoagulant formation Catalysis F5 F5ac PROCa Blood plasma v18 F5a degradation Degradation F5a PROCa-PROS1 Blood plasma/ endothelial membrane v19 F10 activation (1) Catalysis F10 F10a F3-F7a Endothelial membrane v20 F10 actication (2) Catalysis F10 F10a F9a-F8a Platelet membrane v21 F10 activation (3) Catalysis F10 F10a F9a Blood plasma?? v22 F10a degradation Degradation F10a Blood plasma v23 F10a-F5a complex assembly Complex F10a, F5a F10a-F5a Ca2+, neg Platelet assembly phospholipid membrane v24 PROC activation (1) Catalysis PROC PROCa F2a Blood plasma v25 PROS1-C4BP complex assembly Complex PROS1, C4BP PROS1-C4BP Blood plasma assembly v26 PROCa-PROS1 complex assembly Complex PROCa, PROS1 PROCa-PROS1 Ca2+, neg Platelet assembly phospholipid membrane v27 PROCa-PROS1-F5ac complex Complex PROCa-PROS1, PROCa-PROS1- Ca2+, neg Platelet assembly assembly F5ac F5ac phospholipid membrane v28 F13 activation Catalysis F13 F13a F2a,Ca2+ (at Blood plasma least 1 mM) v29 F12 activation (1) Catalysis F12 F12a F12a, negative Negative phospholipds surface v30 F12 activation (2) Catalysis F12 F12a KLKB1a Blood plasma v31 F11 activation (3) Catalysis F12 F12a KNG1 Blood plasma v32 F12a degradation Degradation F12a Blood plasma?? v33 KLKB1 activation Catalysis KLKB1 KLKB1a F12a Blood plasma v34 F11 activation (1) Catalysis F11 F11a F12a Blood plasma v35 F11 activation (2) Catalysis F11 F11a F2a, negative Negative phospholipids surface v36 F11 activation (3) Catalysis F11 F11a F11a, negative Negative phospholipids surface v37 F11a degradation Degradation F11a Blood plasma v38 CPB2 activation (1) Catalysis CPB2 CPB2a F2a Blood plasma v39 CPB2a degradation Degradation CPB2a Blood plasma v40 F10a-TFPI complex assembly Complex TFPI, F10a F10a-TFPI Blood plasma?? assembly v41 F10a-F3-F7a-TFPI complex Complex F10a-TFPI, F10a-F3-F7a- Ca2+ Endothelial assembly assembly F3-F7a TFPI membrane v42 F3-F7a-TFPI complex assembly Complex F3-F7a, TFPI F3-F7a-TFPI Endothelial assembly membrane v43 F11a-SERPINC1 complex assembly Complex F11a, SERPINC1 F11a-SERPINC1 SERPIND1 Blood plasma assembly v44 F12a-SERPINC1 complex assembly Complex F12a, SERPINC1 F12a SERPINC1 SERPIND1 Blood plasma assembly v45 F9a-SERPINC1 complex assembly Complex F9a, SERPINC1 F9a-SERPINC1 SERPIND1 Blood plasma assembly v46 F2a-SERPINC1 complex assembly Complex F2a, SERPINC1 F2a-SERPINC1 SERPIND1 Blood plasma assembly v47 F10a-SERPINC1 complex assembly Complex F10a, SERPINC1 F10a-SERPINC1 SERPIND1 Blood plasma assembly v48 F3-F7a-SERPINC1 complex assembly Complex F3-F7a, SERPINC1 F3-F7a-SERPINC1 SERPIND1 Blood plasma assembly v49 PROCa-SERPINA1 complex assembly Complex PROCa, SERPINA1 PROCa-SERPINA1 Blood plasma assembly v50 PROCa-SERPINA5 complex assembly Complex PHOCa, SERPINA5 PROCa-SERPINA5 Heparin Blood plasma assembly dependent v51 F2a-SERPINA5 complex assembly Complex F2a, SERPINA5 F2a-SERPINA5 Heparin Blood plasma assembly dependent v52 F10a-SERPINA5 complex assembly Complex F10a, SERPINA5 F10a-SERPINA5 Heparin Blood plasma assembly dependent v53 KLKB1a-SERPINA5 complex assembly Complex KLKB1a, KLKB1a- Blood plasma?? assembly SERPINA5 SERPINA5 v54 PROZ-SERPINA10 complex assembly Complex PHOZ, SERPINA10 PROZ-SERPINA10 Blood plasma assembly v55 F9a-SERPINA10 complex assembly Complex F9a, SERPINA10 F9a-SERPINA10 Blood plasma assembly v56 F10a-PROZ-SERPINA10 complex Complex PROZ- F10a-PROZ- Ca2+, Membrane assembly assembly SERPINA10, F10a SERPINA10 Phospholipids v57 F11a-SERPINA10 complex assembly Complex F11a, SERPINA10 F11a-SERPINA10 Blood plasma assembly v58 PROCa-SERPINE1 complex assembly Complex PROCa, SERPINE1 PROCa-SERPINE1 Blood plasma?? assembly v59 F2a-SERPINE1 complex assembly Complex F2a, SERPINE1 F2a-SERPINE1 Blood plasma assembly v60 VTN-SERPINE1 complex assembly Complex VTN, SERPINE1 VTN-SERPINE1 Membrane assembly surface v61 F2a-VTN-SERPINE1 complex Complex F2a, VTN-SERPINE1 F2a-VTN-SERPINE1 Membrane assembly assembly surface v62 CPB2a-SERPINE1 complex assembly Complex CPB2a, SERPME1 CPB2a-SERPINE1 Blood plasma assembly v63 SERPINE1 degradation Degradation SERPINE1 Blood plasma?? v64 F11a-SERPINE1 complex assembly Complex F11a, SERPING1 F11a-SERPING1 Blood plasma?? assembly v65 F12a-SERPING1 complex assembly Complex F12a, SERPING1 F12a-SERPING1 Blood plasma?? assembly v66 KLKB1-SERPING1 complex assembly Complex KLKB1a, SERPING1 KLKB1a-SERPING1 Blood plasma?? assembly v67 F2a-α2-M complex assembly Complex F2a, α2-M F2a-α2-M assembly v68 Substrate catalysis Catalysis subs subsa F2a v69 Substrate catalysis Catalysis subs subsa F2a-α2-M

(56) TABLE-US-00002 TABLE 2 All reaction mechanisms incorporated in the computer model of the fibrin polymerization. Reac- Cofactors/ tion Name Type Substrates Products Catalyst Reaction site v70 FpA cleavage from Fg Catalysis Fg desAA-Fg, 2 FpA F2a Blood plasma v71 FpB cleavage from Fg Catalysis Fg desBB-Fg, 2 FpB F2a Blood plasma v72 FpA cleavage from desAA-Fg Catalysis desAA-Fg Fn, 2 FpA F2a Blood plasma v73 FpB cleavage from desBB-Fg Catalysis desBB-Fg Fn, 2 FpB F2a Blood plasma v74 FpA cleavage from Fg-F2a Catalysis Fg-F2a desAA-Fg-F2a F2a Blood plasma v75 FpB cleavage from Fg-F2a Catalysis Fg-F2a desBB-Fg-F2a F2a Blood plasma v76 Protofibril formation/growth Complex assembly* P.sub.n, P.sub.m P.sub.n+m Blood plasma v77 Fiber formation/growth Complex assembly** F.sub.o, F.sub.p F.sub.n+m Fn Blood plasma *P.sub.n + P.sub.m .fwdarw.P.sub.n+m∀n + m ≧ 29, n > 0, m > 0 **F.sub.o + F.sub.p .fwdarw.P.sub.n+m ∀o + p ≧ 9, o > 0, p > 0

(57) TABLE-US-00003 TABLE 3 All reaction mechanisms incorporated in the computer model regarding the effect of unfractionated heparin (UFH) and low-molecular weight heparin (LMWH) on the function of AT-III. It should be noted that in this table the official gene symbol of AT-III (SERPINC1) is used instead of the popular scientific names. Reac- Cofactors/ tion Name Type Substrates Products Catalyst Reaction site v78 SERPINC1-UFH complex assembly Complex SERPINC1, UFH SERPINC1-UFH Blood plasma assembly v79 F11a-SERPINC1-UFH complex assembly Complex F11a, SERPINC1-UFH F11a-SERPINC1-UFH Blood plasma assembly v80 F9a-SERPINC1-UFH complex assembly Complex F9a, SERPINC1-UFH F9a-SERPINC1-UFH Blood plasma assembly v81 F2a-SERPINC1-UFH complex assembly Complex F2a, SERPINC1-UFH F2a-SERPINC1-UFH Blood plasma assembly v82 F10a-SERPINC1-UFH complex assembly Complex F10a, SERPINC1-UFH F10a-SERPINC1-UFH Blood plasma assembly v83 F3-F7a-SERPINC1-UFH complex assembly Complex F3-F7a, SERPINC1- F3-F7a-SERPINC1- Blood plasma assembly UFH UFH v84 F10a-F5a-SERPINC1-UFH complex Complex F10a-F5a, SERPINC1- F10a-F5a-SERPINC1- Blood plasma assembly assembly UFH UFH v85 SIRPINC1-LMWH complex assembly Complex SERPINC1, LMWH SERPINC1-LMWH Blood plasma assembly v86 F11a-SERPINC1- LMWH complex assembly Complex F11a, SERPINC1- F11a-SERPINC1-LMWH Blood plasma assembly LMWH v87 F9a-SERPINC1-LMWH complex assembly Complex F9a, SERPINC1- F9a-SERPINC1-LMWH Blood plasma assembly LMWH v88 F2a-SERPINC1-LMWH complex assembly Complex F2a, SERPINC1- F2a-SERPINC1-LMWH Blood plasma assembly LMWH v89 F10a-SERPINC1-LMWH complex assembly Complex F10a, SERPINC1- F10a-SERPINC1- Blood plasma assembly LMWH LMWH v90 F3-F7a-SERPINC1-LMWH complex Complex F3-F7a, SERPINC1- F3-F7a-SERPINC1- Blood plasma assembly assembly LMWH LMWH v91 F10a-F5a-SERPINC1-LMWH complex Complex F10a-F5a, SERPINC1- F10a-F5a-SERPINC1- Blood plasma assembly assembly LMWH LMWH

(58) TABLE-US-00004 TABLE 4 The ordinary differential equations incorporated in the computer model regarding the PK effort of unfractionated heparin (UFH) and low-molecular weight heparin (LMWH). All states are described in more detail in the last column, all other entitles in the equations (e.g. IV, keUFH, kaLMWH) are model constants. Reaction Name Equation Description PKPD1 UFH in blood compartment $\frac{d\left[\mathrm{UFH}\right]}{\mathrm{dt}}=\frac{\mathrm{IV}}{{\mathrm{Vd}}_{\mathrm{UFH}}}-k_{}\left[\mathrm{UFH}\right]$ [UFH]: concentration of UFH in blood compartment PKPD2 LMWH in absorption compartment $\frac{{\mathrm{dA}}_{\mathrm{LMWH}}}{\mathrm{dt}}=-k_{}{A}_{\mathrm{LMWH}}$ A.sub.LMWH: amount of LMWH in absorption compartment PKPD3 LMWH in blood compartment $\begin{array}{c}\frac{d\left[\mathrm{LMWH}\right]}{\mathrm{dt}}=k_{}\frac{A_{\mathrm{LMWH}}}{{\mathrm{Vc}}_{\mathrm{LMWH}}}-\\ k_{}\left[\mathrm{LMWH}\right]+\\ k_{,\mathrm{LMWH}}\frac{A_{\mathrm{LMWH},p}}{{\mathrm{Vc}}_{\mathrm{LMWH}}}-\\ k_{\mathrm{eLMWH}}\left[\mathrm{LMWH}\right]\end{array}\hspace{1em}$ [LMWH]: concentration of LMWH in blood compartment PKPD4 LMWH in peripheral compartment 0$\begin{array}{c}\frac{{\mathrm{dA}}_{\mathrm{LMWH},p}}{\mathrm{dt}}=k_{,\mathrm{LMWH}}\left[\mathrm{LMWH}\right]\\ {\mathrm{Vc}}_{\mathrm{LMWH}}-\\ k_{,\mathrm{LMWH}}{A}_{\mathrm{LMWH},p}\end{array}\hspace{1em}$ A.sub.LMHH,p: amount of LMWH in peripheral compartment

(59) TABLE-US-00005 TABLE 5 The ordinary differential equations incorporated in the computer model regarding the PK/PD effect of warfarin. All states described in more detail in the last column, all other entitles in the equations (e.g. k.sub.a, k.sub.e, V.sub.d, VK.sub.prod) are model constants. Reaction Name Equation Description PKPD5  Warfarin absorption $\frac{{\mathrm{dA}}_{\mathrm{warf}}}{\mathrm{dt}}=-k_a{A}_{\mathrm{warf}}$ A.sub.warf: amount of warfarin in the absorption compartment PKPD6  Warfarin in blood compartment $\frac{d\left[\mathrm{warf}\right]}{\mathrm{dt}}=k_a\frac{A_{\mathrm{warf}}}{V_d}-k_{}\left[\mathrm{warf}\right]$ [warf]: concentration of warfarin PKPD7  Vitamin K in peripheral compartment $\frac{{\mathrm{dA}}_{\mathrm{VK},p}}{\mathrm{dt}}=k_{}\left[\mathrm{VK}\right]\mathrm{VcK}-k_{21}{A}_{\mathrm{VK},p}$ A.sub.VK,p: amount of vitamin K in peripheral compartment PKPD8  Vitamin K in blood compartment $\begin{array}{c}\frac{d\left[\mathrm{VK}\right]}{\mathrm{dt}}={\mathrm{VK}}_{\mathrm{prod}}-\mathrm{dvk}2\left[\mathrm{VK}\right]\\ \left(1-\frac{I_{\max }\left[\mathrm{warf}\right]}{{\mathrm{IC}}_{}+\left[\mathrm{warf}\right]}\right)-\\ \mathrm{dvk}\left[\mathrm{VK}\right]+\\ \mathrm{dvko}\left[\mathrm{VKO}\right]\\ \left(1-\frac{I_{\max }\left[\mathrm{warf}\right]}{{\mathrm{IC}}_{}+\left[\mathrm{warf}\right]}\right)-\\ k_{12}\left[\mathrm{VK}\right]+k_{21}\frac{A_{\mathrm{VK},p}}{\mathrm{VcK}}\end{array}\hspace{1em}$ [VK[: concentration of vitamin K in blood compartment PKPD9  Vitamin KH.sub.2 in blood compartment $\begin{array}{c}\frac{d\left[{\mathrm{VKH}}_2\right]}{\mathrm{dt}}=\mathrm{dvk}2\left[\mathrm{VK}\right]\\ \left(1-\frac{I_{\max }\left[\mathrm{warf}\right]}{{\mathrm{IC}}_{50}+\left[\mathrm{warf}\right]}\right)-\\ \mathrm{dvkh}2\left[{\mathrm{VKH}}_z\right]\end{array}\hspace{1em}$ [VKH.sub.2]: concentration of KH.sub.2 in blood compartment PKPD10 Vitamin KO in blood compartment $\begin{array}{c}\frac{d\left[{\mathrm{VKH}}_2\right]}{\mathrm{dt}}=\mathrm{dvk}2\left[\mathrm{VK}\right]\\ \left(1-\frac{I_{\max }\left[\mathrm{warf}\right]}{{\mathrm{IC}}_{50}+\left[\mathrm{warf}\right]}\right)-\\ \mathrm{dvkh}2\left[{\mathrm{VKH}}_2\right]\end{array}\hspace{1em}$ [VKH.sub.2]: concentration of KO in blood compartment PKPD11 F2 in blood compartment $\frac{d\left[F2\right]}{\mathrm{dt}}=\mathrm{pF}2-\mathrm{dF}2\left[F2\right]$ [F2]: concentration of F2 in blood compartment PKPD12 F7 in blood compartment $\frac{d\left[F7\right]}{\mathrm{dt}}=\mathrm{pF}7-\mathrm{dF}7\left[F7\right]$ [F7]: concentration of F2 in blood compartment PKPD13 F8 in blood compartment $\frac{d\left[F9\right]}{\mathrm{dt}}=\mathrm{pF}9-\mathrm{dF}9\left[F9\right]$ [F9]: concentration of F2 in blood compartment

(60) FIG. 4 schematically shows a user interface (100) for visualizing a bridge therapy process. The user interface comprises a receiving arrangement (102) which is configured to receive coagulation data (111). Coagulation data describe a haemostatic situation of the examined blood circulation of a patient. Furthermore, the user interface (100) is configured to display the haemostatic situation of said blood or said blood circulation, for example by a displaying device (103). Furthermore, the user interface is configured to display a first measured value of a first type of blood test, which first measured value indicates a first effect of a heparin-like drug on the haemostatic situation. In addition the user interface is configured to simultaneously display a second measured value of a second type of blood test, which second measured value indicates a second effect of a vitamin K type antagonist type anticoagulant on the haemostatic situation.

(61) The graphical representation which is used to depict said first measured value and said second measured value is exemplarily shown by element (105). It may be seen as responding to the X and Y coordinate system shown in FIG. 6 on the upper right hand side. Furthermore, the displaying device (103) comprises an area (106) in which constant personalized or individual data regarding the patient or the haemostatic function are displayed or can also be entered by a user. In the second zone (107), individual patient data may be displayed and may also be entered by a user. This may for example be an INR value or may also be an amount of anti-coagulant drug which is desired by the user to be checked by the mathematical model that can be performed by the user interface (100). Furthermore, a calculation arrangement (110) is shown wherein the calculation arrangement is configured to calculate a prediction of a haemostatic situation of the blood circulation by combining a biochemical model of the blood circulation and a pharmacodynamical model of the blood circulation.

(62) As the user interface and also the previously presented methods are not applied directly at the patient, no blood circulation or patient is shown in FIG. 4.

(63) The calculation arrangement (110) comprises a program element (104) for visualizing a bridge therapy process, the program element itself may furthermore comprise a biochemical model (108) and a pharmacokinetic or pharmacodynamical model (109) as has been described above and will be described later. Furthermore, a computer-readable medium (112) is shown which is communicating with the receiving section (102) in order to transmit for example coagulation data stored on the storage device or also transmit a program element which was stored on the computer-readable medium.

(64) Furthermore, user interface (100) comprises an entering device (101) in which the user might enter values or also amended administration plan to check how such an amendment would amend the time-resolved progression of the risk of bleeding or risk of thrombosis of the examined blood circulation.

(65) Therein the calculation arrangement (110) is adapted to perform the calculation steps that have been disclosed in the context of the mathematical model which comprises the biochemical model and the pharmacodynamical model. For example, calculating arrangement (110) can be adapted to perform the steps (S9), (S10), (S11) and (S12) as has been described with regard to FIGS. 3a, 3b and 3c. Furthermore, the calculation arrangement (110) may be adapted, if desired, to automatically suggest an application of a coagulant or procoagulant and/or an anti-coagulant to a user.

(66) Additionally such a user interface may be configured to receive coagulation data from different points of time, wherein the user interface may be further configured to display a time-resolved progression of the risk of coagulation and the risk of bleeding of said blood circulation in said graphical representation.

(67) FIG. 5 schematically shows how a user interface (503) might be connected to the other aspects of the invention. User interface (503) might be used in order to receive clinical measurement data (501), for example INR, aPTT, and vitamin K-proteins activity. Such current clinical measurements and previously measured values may be presented by the user interface (503) to the physician. Furthermore, a software layer may be comprised which is depicted by (504). This software layer might be a connector to other IT services in, for example, a hospital IT system. For example a drug ordering system may be connected in such a way to the user interface. The mathematical model, which is used for the calculation of the prediction, is shown in (505). The mathematical model may alternatively be stored in the user interface or also in an external data storage device. (502) depicts that the user or a clinician may input several values which will be explained in detail with regard to the following FIG. 6.

(68) FIG. 6 schematically shows a graphical representation (600) used by a user interface according to an exemplary embodiment of the present invention. The user interface displays an X and Y coordinate system (603) in which the X axis (606) is embodied as an aPTT axis and a Y axis (607) is embodied as an INR axis. However, other test may be used, if desired. In this exemplary representation, five distinct points (609) within that coordinate system are depicted. Between those distinct points in time, the bridge path (610) of the specific patient is shown. Therefore, it can be gathered from the graphical representation (600) how the bleeding and thrombosis risks evolve over time. Furthermore, rectangular areas (608) are displayed to the user in this example which show or depict the region in which a safe haemostatic situation is indicated. This simultaneous display of PTT and INR enables the clinician to monitor the bridge therapy and safely compensate for an incorrect anticoagulation. A solution is offered to the treating physician by means of a graphical user interface as shown in (600). Furthermore, a field for recommended action (604) is presented. Several input possibilities (611) are provided for the user, where he may for example amend the currently provided administration of drugs. By means of button (612) user may chose which potential amendment he wants to have calculated by the underlying mathematical model and the predicted effect of which he wants displayed afterwards on the above zone (603). Furthermore, zone (602) is shown of the optical presentation on which several test values can be entered by the user. These test values (605) may then be submitted to an underlying mathematical model to calculate a prediction.

(69) In zone (601) of the graphical representation individual and/or personal data can be displayed to the user and may also be entered by the user. For example, name, age and weight of the patient may be displayed and/or entered. Or the bridge type may be displayed to the user in order to know which bridging process is going on at the moment. In FIG. 6, the bridge type from VKA to UFH is presented.

(70) Thus, in FIG. 6 the steps of graphically representing the first value as a point on the first axis is depicted which may seen as step S7. Further the step of graphically representing the second value as a point on the second axis is depicted which may be seen as step S8.